tSNE was first introduced by [1]}.
Like PCA, tSNE is an unsupervised algorithm but the projection is non-linear.
tSNE optimizes the pairwise similarities in the low-dimensional space to be similar to those in the original high-dimensional data using a cost function.
We use the tSNE implementation from the sklearn manifold module.
For stability of the tSNE algorithm, we initialize the learned embedding with PCA.
